However, one of the major obstacles for exploiting SGD in optimizing combustion models is the lack of software ecosystems that can efficiently and accurately compute the gradient of simulation output to model parameters. For instance, the finite difference method (often termed the "brute-force method") usually suffers both computational inefficiency due to the cost scaling with the number of parameters and inaccuracy due to the truncation error [1]}. Conversely, gradient evaluation methods based on auto-differentiation (AD) have shown both efficiency and accuracy in the training of large-scale deep neural network models. Many open-source AD packages have been developed in the last decade, including TensorFlow [2]} and Jax [3]} backed by Google, PyTorch [4]} backed by Facebook, ForwardDiff.jl [5]} and Zygote.jl [6]} in Julia. To this end, this work presents a simulation package Arrhenius.jl and techniques that enable SGD for combustion model optimization, and applies them to the modeling of F-24, which is a variant of Jet-A with additives.
